{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-23T09:24:15.427545Z",
     "start_time": "2021-04-23T09:24:15.015898Z"
    },
    "execution": {
     "iopub.execute_input": "2021-06-26T05:59:16.655121Z",
     "iopub.status.busy": "2021-06-26T05:59:16.654540Z",
     "iopub.status.idle": "2021-06-26T05:59:16.660517Z",
     "shell.execute_reply": "2021-06-26T05:59:16.659136Z",
     "shell.execute_reply.started": "2021-06-26T05:59:16.655074Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# https://github.com/lemonhu/NER-BERT-pytorch/tree/master/data/msra"
   ]
  },
  {
   "attachments": {
    "558b9f35-d120-4635-ab3e-e848eb3fae2d.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:558b9f35-d120-4635-ab3e-e848eb3fae2d.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-23T13:34:52.838479Z",
     "start_time": "2021-04-23T13:34:52.455079Z"
    },
    "execution": {
     "iopub.execute_input": "2021-04-24T12:48:16.632349Z",
     "iopub.status.busy": "2021-04-24T12:48:16.631788Z",
     "iopub.status.idle": "2021-04-24T12:48:16.721436Z",
     "shell.execute_reply": "2021-04-24T12:48:16.720983Z",
     "shell.execute_reply.started": "2021-04-24T12:48:16.632302Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import codecs\n",
    "import numpy as np\n",
    "\n",
    "# 我们爱数据科学。\n",
    "# O O O B-ORG I-ORG I-ORG I-ORG O"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-23T13:34:54.138977Z",
     "start_time": "2021-04-23T13:34:53.477451Z"
    },
    "execution": {
     "iopub.execute_input": "2021-04-24T12:48:17.260018Z",
     "iopub.status.busy": "2021-04-24T12:48:17.259481Z",
     "iopub.status.idle": "2021-04-24T12:48:17.662302Z",
     "shell.execute_reply": "2021-04-24T12:48:17.661766Z",
     "shell.execute_reply.started": "2021-04-24T12:48:17.259971Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "tag_type = ['O', 'B-ORG', 'I-ORG', 'B-PER', 'I-PER', 'B-LOC', 'I-LOC']\n",
    "# B-ORG I-ORG 机构的开始位置和中间位置\n",
    "# B-PER I-PER 人物名字的开始位置和中间位置\n",
    "# B-LOC I-LOC 位置的开始位置和中间位置\n",
    "\n",
    "train_lines = codecs.open('msra/train/sentences.txt').readlines()\n",
    "train_lines = [x.replace(' ', '').strip() for x in train_lines]\n",
    "\n",
    "train_tags = codecs.open('msra/train/tags.txt').readlines()\n",
    "train_tags = [x.strip().split(' ') for x in train_tags]\n",
    "train_tags = [[tag_type.index(x) for x in tag] for tag in train_tags]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-24T12:48:18.742526Z",
     "iopub.status.busy": "2021-04-24T12:48:18.741982Z",
     "iopub.status.idle": "2021-04-24T12:48:18.752852Z",
     "shell.execute_reply": "2021-04-24T12:48:18.752204Z",
     "shell.execute_reply.started": "2021-04-24T12:48:18.742480Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_lines, train_tags = train_lines[:20000], train_tags[:20000] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-23T14:53:47.364732Z",
     "start_time": "2021-04-23T14:53:47.359559Z"
    },
    "execution": {
     "iopub.execute_input": "2021-04-24T12:48:19.125637Z",
     "iopub.status.busy": "2021-04-24T12:48:19.125127Z",
     "iopub.status.idle": "2021-04-24T12:48:19.331877Z",
     "shell.execute_reply": "2021-04-24T12:48:19.330797Z",
     "shell.execute_reply.started": "2021-04-24T12:48:19.125592Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "val_lines = codecs.open('msra/val/sentences.txt').readlines()\n",
    "val_lines = [x.replace(' ', '').strip() for x in val_lines]\n",
    "\n",
    "val_tags = codecs.open('msra/val/tags.txt').readlines()\n",
    "val_tags = [x.strip().split(' ') for x in val_tags]\n",
    "val_tags = [[tag_type.index(x) for x in tag] for tag in val_tags]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-23T13:35:14.896288Z",
     "start_time": "2021-04-23T13:35:02.759708Z"
    },
    "execution": {
     "iopub.execute_input": "2021-04-24T12:48:21.871049Z",
     "iopub.status.busy": "2021-04-24T12:48:21.870502Z",
     "iopub.status.idle": "2021-04-24T12:48:31.550685Z",
     "shell.execute_reply": "2021-04-24T12:48:31.550074Z",
     "shell.execute_reply.started": "2021-04-24T12:48:21.871003Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from transformers import BertTokenizer\n",
    "tokenizer = BertTokenizer.from_pretrained('bert-base-chinese')\n",
    "train_encoding = tokenizer(list(train_lines), truncation=True, padding=True, max_length=64)\n",
    "val_encoding = tokenizer(list(val_lines), truncation=True, padding=True, max_length=64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-23T13:35:17.120298Z",
     "start_time": "2021-04-23T13:35:16.751314Z"
    },
    "execution": {
     "iopub.execute_input": "2021-04-24T12:49:16.594534Z",
     "iopub.status.busy": "2021-04-24T12:49:16.593957Z",
     "iopub.status.idle": "2021-04-24T12:49:17.018653Z",
     "shell.execute_reply": "2021-04-24T12:49:17.018088Z",
     "shell.execute_reply.started": "2021-04-24T12:49:16.594488Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "from torch.utils.data import Dataset, DataLoader, TensorDataset\n",
    "\n",
    "class TextDataset(Dataset):\n",
    "    def __init__(self, encodings, labels):\n",
    "        self.encodings = encodings\n",
    "        self.labels = labels\n",
    "        \n",
    "    def __getitem__(self, idx):\n",
    "        item = {key: torch.tensor(val[idx])[:64] for key, val in self.encodings.items()}\n",
    "        # 字级别的标注\n",
    "        item['labels'] = torch.tensor([0] + self.labels[idx] + [0] * (63-len(self.labels[idx])))[:64]\n",
    "        return item\n",
    "    \n",
    "    def __len__(self):\n",
    "        return len(self.labels)\n",
    "\n",
    "train_dataset = TextDataset(train_encoding, train_tags[:])\n",
    "test_dataset = TextDataset(val_encoding, val_tags[:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-24T12:49:25.212607Z",
     "iopub.status.busy": "2021-04-24T12:49:25.212040Z",
     "iopub.status.idle": "2021-04-24T12:49:25.226301Z",
     "shell.execute_reply": "2021-04-24T12:49:25.225693Z",
     "shell.execute_reply.started": "2021-04-24T12:49:25.212561Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'input_ids': tensor([ 101, 1963,  862, 6237, 1104, 6639, 4413, 4518, 7270, 3309, 2100, 1762,\n",
       "         4638, 6436, 1914, 4757, 4688, 8024, 7028, 2920, 3212, 3189, 3823, 7305,\n",
       "         6639, 4413, 4638, 7413, 7599, 8024, 2768,  711, 1921, 3823, 6639, 1781,\n",
       "          677,  678, 1079, 1912, 1168, 1905, 6379, 6389, 4638, 6413, 7579,  511,\n",
       "          102,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
       "            0,    0,    0,    0]),\n",
       " 'token_type_ids': tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]),\n",
       " 'attention_mask': tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "         1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]),\n",
       " 'labels': tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6,\n",
       "         0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_dataset[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-24T02:30:40.720554Z",
     "iopub.status.busy": "2021-04-24T02:30:40.720381Z",
     "iopub.status.idle": "2021-04-24T02:30:41.476863Z",
     "shell.execute_reply": "2021-04-24T02:30:41.476337Z",
     "shell.execute_reply.started": "2021-04-24T02:30:40.720540Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "for idx in range(len(train_dataset)):\n",
    "    item = train_dataset[idx]\n",
    "    for key in item:\n",
    "        if item[key].shape[0] != 64:\n",
    "            print(key, item[key].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-24T02:30:44.593987Z",
     "iopub.status.busy": "2021-04-24T02:30:44.593460Z",
     "iopub.status.idle": "2021-04-24T02:30:44.702663Z",
     "shell.execute_reply": "2021-04-24T02:30:44.702273Z",
     "shell.execute_reply.started": "2021-04-24T02:30:44.593943Z"
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [],
   "source": [
    "for idx in range(len(test_dataset)):\n",
    "    item = test_dataset[idx]\n",
    "    for key in item:\n",
    "        if item[key].shape[0] != 64:\n",
    "            print(key, item[key].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from transformers import BertForTokenClassification, AdamW, get_linear_schedule_with_warmup\n",
    "model = BertForTokenClassification.from_pretrained('bert-base-chinese', num_labels=7)\n",
    "\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "model.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-23T13:35:18.744186Z",
     "start_time": "2021-04-23T13:35:18.183277Z"
    },
    "execution": {
     "iopub.execute_input": "2021-04-24T03:13:20.803134Z",
     "iopub.status.busy": "2021-04-24T03:13:20.802591Z",
     "iopub.status.idle": "2021-04-24T03:13:28.896245Z",
     "shell.execute_reply": "2021-04-24T03:13:28.895455Z",
     "shell.execute_reply.started": "2021-04-24T03:13:20.803086Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at bert-base-chinese were not used when initializing BertForTokenClassification: ['cls.predictions.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.decoder.weight', 'cls.seq_relationship.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.LayerNorm.bias']\n",
      "- This IS expected if you are initializing BertForTokenClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing BertForTokenClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
      "Some weights of BertForTokenClassification were not initialized from the model checkpoint at bert-base-chinese and are newly initialized: ['classifier.weight', 'classifier.bias']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'DataLoader' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-4-6b86db8eef56>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mdevice\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"cuda:1\"\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_available\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m\"cpu\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mtrain_loader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDataLoader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_dataset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m32\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshuffle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m \u001b[0mtest_dataloader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDataLoader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_dataset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m32\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshuffle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'DataLoader' is not defined"
     ]
    }
   ],
   "source": [
    "train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)\n",
    "test_dataloader = DataLoader(test_dataset, batch_size=32, shuffle=True)\n",
    "\n",
    "optim = AdamW(model.parameters(), lr=5e-5)\n",
    "total_steps = len(train_loader) * 1\n",
    "scheduler = get_linear_schedule_with_warmup(optim, \n",
    "                                            num_warmup_steps = 0, # Default value in run_glue.py\n",
    "                                            num_training_steps = total_steps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-23T13:37:36.436445Z",
     "start_time": "2021-04-23T13:35:34.993720Z"
    },
    "execution": {
     "iopub.execute_input": "2021-04-24T02:36:53.191816Z",
     "iopub.status.busy": "2021-04-24T02:36:53.191231Z",
     "iopub.status.idle": "2021-04-24T02:45:04.763608Z",
     "shell.execute_reply": "2021-04-24T02:45:04.763157Z",
     "shell.execute_reply.started": "2021-04-24T02:36:53.191764Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------Epoch: 0 ----------------\n",
      "0.2490234375 1.7682684659957886\n",
      "0.984375 0.08177318423986435\n",
      "0.9794921875 0.10324091464281082\n",
      "0.9853515625 0.05329243093729019\n",
      "0.974609375 0.0980382040143013\n",
      "epoth: 0, iter_num: 100, loss: 0.0187, 16.00%\n",
      "0.98486328125 0.03968340530991554\n",
      "0.97802734375 0.13193301856517792\n",
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      "0.99462890625 0.030755499377846718\n",
      "0.96435546875 0.15113528072834015\n",
      "epoth: 0, iter_num: 200, loss: 0.0309, 32.00%\n",
      "0.98046875 0.05407458916306496\n",
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      "0.96337890625 0.10279929637908936\n",
      "0.9755859375 0.05761774629354477\n",
      "0.99560546875 0.027676289901137352\n",
      "epoth: 0, iter_num: 300, loss: 0.0450, 48.00%\n",
      "0.99072265625 0.04058162495493889\n",
      "0.99169921875 0.03472297638654709\n",
      "0.9833984375 0.0438205860555172\n",
      "0.9912109375 0.02867487072944641\n",
      "0.98486328125 0.05152392014861107\n",
      "epoth: 0, iter_num: 400, loss: 0.0556, 64.00%\n",
      "0.99267578125 0.020176555961370468\n",
      "0.9833984375 0.03304135426878929\n",
      "0.99365234375 0.021856384351849556\n",
      "0.99609375 0.025094078853726387\n",
      "0.98388671875 0.02991459146142006\n",
      "epoth: 0, iter_num: 500, loss: 0.0585, 80.00%\n",
      "0.98388671875 0.06794300675392151\n",
      "0.9970703125 0.0023129573091864586\n",
      "0.98291015625 0.047733839601278305\n",
      "0.99365234375 0.01836448535323143\n",
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      "epoth: 0, iter_num: 600, loss: 0.0329, 96.00%\n",
      "0.9892578125 0.02512132190167904\n",
      "0.97802734375 0.030762100592255592\n",
      "Epoch: 0, Average training loss: 0.0778\n",
      "Accuracy: 0.9885\n",
      "Average testing loss: 0.0397\n",
      "-------------------------------\n",
      "------------Epoch: 1 ----------------\n",
      "0.994140625 0.012883431278169155\n",
      "0.9921875 0.028787333518266678\n",
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      "0.9912109375 0.03425929322838783\n",
      "0.98779296875 0.048689037561416626\n",
      "epoth: 1, iter_num: 100, loss: 0.0263, 16.00%\n",
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      "epoth: 1, iter_num: 300, loss: 0.0139, 48.00%\n",
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      "epoth: 1, iter_num: 400, loss: 0.0317, 64.00%\n",
      "0.9892578125 0.030402926728129387\n",
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      "epoth: 1, iter_num: 500, loss: 0.0263, 80.00%\n",
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      "0.97802734375 0.07021111994981766\n",
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      "Epoch: 1, Average training loss: 0.0311\n",
      "Accuracy: 0.9885\n",
      "Average testing loss: 0.0397\n",
      "-------------------------------\n",
      "------------Epoch: 2 ----------------\n",
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      "epoth: 2, iter_num: 300, loss: 0.0204, 48.00%\n",
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      "0.99462890625 0.004402865655720234\n",
      "epoth: 2, iter_num: 400, loss: 0.0411, 64.00%\n",
      "0.99365234375 0.010571631602942944\n",
      "0.98486328125 0.03408976271748543\n",
      "0.98486328125 0.03624831140041351\n",
      "0.98779296875 0.015979593619704247\n",
      "0.98779296875 0.046796899288892746\n",
      "epoth: 2, iter_num: 500, loss: 0.0268, 80.00%\n",
      "0.9951171875 0.02482364885509014\n",
      "0.99365234375 0.026390183717012405\n",
      "0.9931640625 0.02190655842423439\n",
      "0.9931640625 0.014970648102462292\n",
      "0.990234375 0.02842547930777073\n",
      "epoth: 2, iter_num: 600, loss: 0.0222, 96.00%\n",
      "0.99267578125 0.023658493533730507\n",
      "0.982421875 0.015109711326658726\n",
      "Epoch: 2, Average training loss: 0.0312\n",
      "Accuracy: 0.9885\n",
      "Average testing loss: 0.0400\n",
      "-------------------------------\n",
      "------------Epoch: 3 ----------------\n",
      "0.984375 0.017112642526626587\n",
      "0.9921875 0.03326643258333206\n",
      "0.97509765625 0.06490886956453323\n",
      "0.9970703125 0.008432931266725063\n",
      "0.99169921875 0.0269821397960186\n",
      "epoth: 3, iter_num: 100, loss: 0.0286, 16.00%\n",
      "0.99267578125 0.01527893915772438\n",
      "0.97900390625 0.058531519025564194\n",
      "0.98583984375 0.04331326484680176\n",
      "0.9892578125 0.04045962914824486\n",
      "0.9912109375 0.029991429299116135\n",
      "epoth: 3, iter_num: 200, loss: 0.0099, 32.00%\n",
      "0.990234375 0.015588161535561085\n",
      "0.9921875 0.02152552641928196\n",
      "0.98876953125 0.036023806780576706\n",
      "0.99658203125 0.008520455099642277\n",
      "0.98486328125 0.03546988219022751\n",
      "epoth: 3, iter_num: 300, loss: 0.0699, 48.00%\n",
      "0.986328125 0.030712468549609184\n",
      "0.98828125 0.020864032208919525\n",
      "0.99267578125 0.00887017697095871\n",
      "0.99658203125 0.01782744750380516\n",
      "0.99755859375 0.015181810595095158\n",
      "epoth: 3, iter_num: 400, loss: 0.0831, 64.00%\n",
      "0.9912109375 0.011080128140747547\n",
      "0.98876953125 0.02567201666533947\n",
      "0.986328125 0.026015648618340492\n",
      "0.99072265625 0.011677887290716171\n",
      "0.98779296875 0.04932735115289688\n",
      "epoth: 3, iter_num: 500, loss: 0.0104, 80.00%\n",
      "0.99560546875 0.011631021276116371\n",
      "0.990234375 0.02875502221286297\n",
      "0.9912109375 0.04603218287229538\n",
      "0.978515625 0.04179846867918968\n",
      "0.9931640625 0.018458472564816475\n",
      "epoth: 3, iter_num: 600, loss: 0.0429, 96.00%\n",
      "0.9921875 0.022404665127396584\n",
      "0.99169921875 0.03245164453983307\n",
      "Epoch: 3, Average training loss: 0.0311\n",
      "Accuracy: 0.9885\n",
      "Average testing loss: 0.0397\n",
      "-------------------------------\n"
     ]
    }
   ],
   "source": [
    "from tqdm import tqdm\n",
    "\n",
    "def train():\n",
    "    model.train()\n",
    "    total_train_loss = 0\n",
    "    iter_num = 0\n",
    "    total_iter = len(train_loader)\n",
    "    for idx, batch in enumerate(train_loader):\n",
    "        optim.zero_grad()\n",
    "        \n",
    "        input_ids = batch['input_ids'].to(device)\n",
    "        attention_mask = batch['attention_mask'].to(device)\n",
    "        labels = batch['labels'].to(device)\n",
    "        outputs = model(input_ids, attention_mask=attention_mask, labels=labels)\n",
    "            # loss = outputs[0]\n",
    "\n",
    "        loss = outputs.loss\n",
    "        \n",
    "        if idx % 20 == 0:\n",
    "            with torch.no_grad():\n",
    "                # 64 * 7\n",
    "                print((outputs[1].argmax(2).data == labels.data).float().mean().item(), loss.item())\n",
    "        \n",
    "        total_train_loss += loss.item()\n",
    "        loss.backward()\n",
    "        torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)\n",
    "        optim.step()\n",
    "        scheduler.step()\n",
    "\n",
    "        iter_num += 1\n",
    "        if(iter_num % 100==0):\n",
    "            print(\"epoth: %d, iter_num: %d, loss: %.4f, %.2f%%\" % (epoch, iter_num, loss.item(), iter_num/total_iter*100))\n",
    "        \n",
    "    print(\"Epoch: %d, Average training loss: %.4f\"%(epoch, total_train_loss/len(train_loader)))\n",
    "    \n",
    "def validation():\n",
    "    model.eval()\n",
    "    total_eval_accuracy = 0\n",
    "    total_eval_loss = 0\n",
    "    for batch in test_dataloader:\n",
    "        with torch.no_grad():\n",
    "            input_ids = batch['input_ids'].to(device)\n",
    "            attention_mask = batch['attention_mask'].to(device)\n",
    "            labels = batch['labels'].to(device)\n",
    "            outputs = model(input_ids, attention_mask=attention_mask, labels=labels)\n",
    "        loss = outputs.loss\n",
    "        logits = outputs[1]\n",
    "\n",
    "        total_eval_loss += loss.item()\n",
    "        logits = logits.detach().cpu().numpy()\n",
    "        label_ids = labels.to('cpu').numpy()\n",
    "        total_eval_accuracy += (outputs[1].argmax(2).data == labels.data).float().mean().item()\n",
    "        \n",
    "    avg_val_accuracy = total_eval_accuracy / len(test_dataloader)\n",
    "    print(\"Accuracy: %.4f\" % (avg_val_accuracy))\n",
    "    print(\"Average testing loss: %.4f\"%(total_eval_loss/len(test_dataloader)))\n",
    "    print(\"-------------------------------\")\n",
    "    \n",
    "\n",
    "for epoch in range(4):\n",
    "    print(\"------------Epoch: %d ----------------\" % epoch)\n",
    "    train()\n",
    "    validation()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-24T02:45:11.533512Z",
     "iopub.status.busy": "2021-04-24T02:45:11.532931Z",
     "iopub.status.idle": "2021-04-24T02:45:12.163488Z",
     "shell.execute_reply": "2021-04-24T02:45:12.162999Z",
     "shell.execute_reply.started": "2021-04-24T02:45:11.533464Z"
    }
   },
   "outputs": [],
   "source": [
    "torch.save(model, 'bert-ner.pt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-23T14:51:18.819401Z",
     "start_time": "2021-04-23T14:51:18.807242Z"
    },
    "execution": {
     "iopub.execute_input": "2021-04-24T03:13:59.374545Z",
     "iopub.status.busy": "2021-04-24T03:13:59.374001Z",
     "iopub.status.idle": "2021-04-24T03:13:59.382786Z",
     "shell.execute_reply": "2021-04-24T03:13:59.382132Z",
     "shell.execute_reply.started": "2021-04-24T03:13:59.374497Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "tag_type = ['O', 'B-ORG', 'I-ORG', 'B-PER', 'I-PER', 'B-LOC', 'I-LOC']\n",
    "\n",
    "def predcit(s):\n",
    "    item = tokenizer([s], truncation=True, padding='longest', max_length=64)\n",
    "    with torch.no_grad():\n",
    "        input_ids = torch.tensor(item['input_ids']).to(device).reshape(1, -1)\n",
    "        attention_mask = torch.tensor(item['attention_mask']).to(device).reshape(1, -1)\n",
    "        labels = torch.tensor([0] * attention_mask.shape[1]).to(device).reshape(1, -1)\n",
    "        \n",
    "        outputs = model(input_ids, attention_mask, labels)\n",
    "        outputs = outputs[0].data.cpu().numpy()\n",
    "        \n",
    "    outputs = outputs[0].argmax(1)[1:-1]\n",
    "    ner_result = ''\n",
    "    ner_flag = ''\n",
    "    \n",
    "    for o, c in zip(outputs,s):\n",
    "        # 0 就是 O，没有含义\n",
    "        if o == 0 and ner_result == '':\n",
    "            continue\n",
    "        \n",
    "        # \n",
    "        elif o == 0 and ner_result != '':\n",
    "            if ner_flag == 'O':\n",
    "                print('机构：', ner_result)\n",
    "            if ner_flag == 'P':\n",
    "                print('人名：', ner_result)\n",
    "            if ner_flag == 'L':\n",
    "                print('位置：', ner_result)\n",
    "                \n",
    "            ner_result = ''\n",
    "        \n",
    "        elif o != 0:\n",
    "            ner_flag = tag_type[o][2]\n",
    "            ner_result += c\n",
    "    return outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-23T14:54:57.736608Z",
     "start_time": "2021-04-23T14:54:57.706272Z"
    },
    "execution": {
     "iopub.execute_input": "2021-04-24T03:13:59.848271Z",
     "iopub.status.busy": "2021-04-24T03:13:59.847730Z",
     "iopub.status.idle": "2021-04-24T03:13:59.869450Z",
     "shell.execute_reply": "2021-04-24T03:13:59.868791Z",
     "shell.execute_reply.started": "2021-04-24T03:13:59.848225Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "位置： 华盛顿\n",
      "位置： 美国\n",
      "位置： 白宫\n",
      "位置： 菲律宾\n",
      "位置： 马拉卡南宫\n"
     ]
    }
   ],
   "source": [
    "s = '整个华盛顿已笼罩在一片夜色之中，一个电话从美国总统府白宫打到了菲律宾总统府马拉卡南宫。'\n",
    "# 识别出句子里面的实体识别（NER）\n",
    "data = predcit(s)\n",
    "\n",
    "# 标注信息：是对每个字进行标注。\n",
    "# 标注信息：任务不同标注不同。\n",
    "#    文本分类：样本 -》 类别，一个句子一个标注\n",
    "#    实体识别：样本 -》 字级别的类别，一个句子\n",
    "\n",
    "# 如 何 解 决 足 球 界 长 期 存 在 的 诸 多 矛 盾 ， 重 振 昔 日 津 门 足 球 的 雄 风 ， 成 为 天 津 足 坛 上 下 内 外 到 处 议 论 的 话 题 。\n",
    "# O O O O O O O O O O O O O O O O O O O O O B-LOC I-LOC O O O O O O O O B-LOC I-LOC O O O O O O O O O O O O O O"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-23T14:54:58.297655Z",
     "start_time": "2021-04-23T14:54:58.291706Z"
    },
    "execution": {
     "iopub.execute_input": "2021-04-24T03:07:44.697612Z",
     "iopub.status.busy": "2021-04-24T03:07:44.697074Z",
     "iopub.status.idle": "2021-04-24T03:07:44.711205Z",
     "shell.execute_reply": "2021-04-24T03:07:44.710752Z",
     "shell.execute_reply.started": "2021-04-24T03:07:44.697565Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "位置： 中国\n",
      "位置： 美国\n"
     ]
    }
   ],
   "source": [
    "s = '人工智能是未来的希望，也是中国和美国的冲突点。'\n",
    "data = predcit(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-23T14:54:42.166525Z",
     "start_time": "2021-04-23T14:54:42.134935Z"
    },
    "execution": {
     "iopub.execute_input": "2021-04-24T03:08:02.948129Z",
     "iopub.status.busy": "2021-04-24T03:08:02.947567Z",
     "iopub.status.idle": "2021-04-24T03:08:02.976157Z",
     "shell.execute_reply": "2021-04-24T03:08:02.975655Z",
     "shell.execute_reply.started": "2021-04-24T03:08:02.948085Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "位置： 海淀\n",
      "人名： 刘涛\n",
      "人名： 王华\n"
     ]
    }
   ],
   "source": [
    "s = '明天我们一起在海淀吃个饭吧，把叫刘涛和王华也叫上。'\n",
    "data = predcit(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-24T03:14:29.774924Z",
     "iopub.status.busy": "2021-04-24T03:14:29.774354Z",
     "iopub.status.idle": "2021-04-24T03:14:29.808248Z",
     "shell.execute_reply": "2021-04-24T03:14:29.807664Z",
     "shell.execute_reply.started": "2021-04-24T03:14:29.774876Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "机构： 同煤集团同生安平煤业公司\n"
     ]
    }
   ],
   "source": [
    "s = '同煤集团同生安平煤业公司发生井下安全事故 19名矿工遇难'\n",
    "data = predcit(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-24T03:14:56.992793Z",
     "iopub.status.busy": "2021-04-24T03:14:56.992229Z",
     "iopub.status.idle": "2021-04-24T03:14:57.046461Z",
     "shell.execute_reply": "2021-04-24T03:14:57.045922Z",
     "shell.execute_reply.started": "2021-04-24T03:14:56.992745Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "机构： 山东省政府办公厅\n",
      "机构： 平邑县玉荣商贸有限公司\n"
     ]
    }
   ],
   "source": [
    "s = '山东省政府办公厅就平邑县玉荣商贸有限公司石膏矿坍塌事故发出通报'\n",
    "data = predcit(s)\n",
    "\n",
    "# 实体抽取\n",
    "# 语义分割是图像"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-24T03:16:04.482434Z",
     "iopub.status.busy": "2021-04-24T03:16:04.481856Z",
     "iopub.status.idle": "2021-04-24T03:16:04.514867Z",
     "shell.execute_reply": "2021-04-24T03:16:04.514221Z",
     "shell.execute_reply.started": "2021-04-24T03:16:04.482387Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "位置： 黑龙江\n",
      "机构： 龙煤集团\n"
     ]
    }
   ],
   "source": [
    "s = '[新闻直播间]黑龙江:龙煤集团一煤矿发生火灾事故'\n",
    "data = predcit(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-21T12:43:55.693007Z",
     "iopub.status.busy": "2021-05-21T12:43:55.692438Z",
     "iopub.status.idle": "2021-05-21T12:43:55.702639Z",
     "shell.execute_reply": "2021-05-21T12:43:55.701964Z",
     "shell.execute_reply.started": "2021-05-21T12:43:55.692958Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['and', 'and this', 'document', 'document is', 'first', 'first document', 'is', 'is the', 'is this', 'one', 'second', 'second document', 'the', 'the first', 'the second', 'the third', 'third', 'third one', 'this', 'this document', 'this is', 'this the']\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "corpus = [\n",
    "    'This is the first document.',\n",
    "    'This document is the second document.',\n",
    "    'And this is the third one.',\n",
    "    'Is this the first document?',\n",
    "]\n",
    "\n",
    "vectorizer2 = CountVectorizer(analyzer='word', ngram_range=(1, 2))\n",
    "X2 = vectorizer2.fit_transform(corpus)\n",
    "print(vectorizer2.get_feature_names())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-21T12:45:16.831784Z",
     "iopub.status.busy": "2021-05-21T12:45:16.831224Z",
     "iopub.status.idle": "2021-05-21T12:45:16.838164Z",
     "shell.execute_reply": "2021-05-21T12:45:16.837324Z",
     "shell.execute_reply.started": "2021-05-21T12:45:16.831736Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<4x22 sparse matrix of type '<class 'numpy.longlong'>'\n",
       "\twith 39 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "CountVectorizer + TfidfTransformer = TfidfVectorizer\n",
    "TfidfVectorizer：直接输入文本"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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